IoT Machine Learning and Artificial Intelligence Services Forecast —
The next wave of Internet of Things (IoT) analytics development will fully converge with the Big Data domain. Simultaneously, the value in the technology stack is shifting beyond the hardware and middleware to analytics and value-added services, such as Machine-Learning (ML) and Artificial Intelligence (AI). According to global tech market advisory firm ABI Research, ML and AI services are estimated to grow within the IoT domain at a CAGR of nearly 40%, reaching US$3.6 billion in 2026.
While COVID-19 impacted many industries, the IoT data analytics market has been less affected. In fact, many newly emerging cloud-native data-enabled analytics vendors have benefited from the COVID-19 pandemic. Since industries are transitioning to “remote everything,” out-of-the-box solutions for remote monitoring, asset management, asset visibility, and predictive maintenance, are in high demand and exemplify market acceleration. Vendors, such as DataRobot, are now easing access to ML and AI tool sets through different deployment options at the edge, on-premises, and the cloud, and through consumption using Platform as a Service (PaaS) and Software as a Service (SaaS). All and all, the COVID-19 pandemic highlighted the importance of rapid deployment solutions, such as hardware-agnostic SaaS.
Companies like AWS, C3, and Google, also have been successful in promoting their products and analytics capabilities (tool sets and environment) by creating centralized repositories for COVID-19 data. Currently, these data lakes are public and are not monetized. However, it is expected that those companies will attempt to use the data lakes to create products for sale to the healthcare market in the future.
From a technology perspective, the data lakes could be the first step for creating and testing data visibility, and streaming analytics services. The COVID-19 pandemic has showcased the public cloud’s healthcare industry ambitions expanding into pharmaceutical, biomedicine, and telemedicine.
Additional report highlights include:
- The next wave of Internet of Things (IoT) analytics development is on real-time analytics and monitoring with closed-loop communication, automated data integration, and advancement of traditional ML/AI.
- The value in the technology stack is shifting beyond the hardware and middleware to analytics and value-added ML and AI services.
- Medical, biomedical, and pharmaceutical, use cases are experiencing an uplift in the IoT analytics as a result of the 2020 COVID-19 pandemic, while the public services market is shifting analytics spend from transportation (smart parking, street lighting, intelligent transportation, etc.) to medical infrastructure and services.
Big data and data analytics might not have a remedy for the virus, but IoT-data-enabled technologies proved essential to lessen public anxiety, to monitor patients, and to prepare the infrastructure for new outbreaks. AI and ML usage accelerated during the pandemic — however, greenfield AI projects saw a significant slowdown.
The AI and ML in the IoT is at its early adoption stage, the lack of the development of data-enabled infrastructure prevented rapid adoption of the ML on operational level when COVID-19 accelerated.
Resources and Notes
These findings are from ABI Research’s IoT Data-Enabled Services: Value Chain, Companies to Watch, and Cloud Wars application analysis report. This report is part of the company’s M2M, IoT & IoE research service, which includes research, data, and analyst insights. Application Analysis reports present an in-depth analysis of key market trends and factors for a specific technology. For more information, visit https://www.abiresearch.com/market-research/product/7778820-iot-data-enabled-services-value-chain-comp/?utm_source=media&utm_medium=email